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Computational Ecology and Software, 2027, 17(1): 30-50
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Article

Effective discrimination of spatial distribution patterns of rare species: Methodological reflections, theoretical framework, and empirical strategies beyond the binary dilemma of Aggregated vs. Random

WenJun Zhang
School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, China

Received 2 March 2026;Accepted 25 July 2026;Published online 12 July 2026;Published 1 March 2027
IAEES

Abstract
The accurate identification of spatial distribution patterns for rare species constitutes a fundamental challenge in community ecology and conservation biology. Rare species typically exhibit stronger spatial aggregation than common species, yet their low abundance renders standard aggregation indices unreliable, creating a persistent difficulty in distinguishing true ecological signals from statistical artifacts. This study systematically examines the methodological limitations that produce the core dilemma: the binary classification of patterns as aggregated or random fails to account for the profound uncertainty inherent in small-sample inference. I develop an innovative three-dimensional analytical framework for the effective discrimination of spatial patterns in rare species, integrating multidimensional rarity typology, scale continuity of pattern, and explicit quantification of estimation uncertainty. Central to this framework is the concept of the spatial pattern continuum, which replaces discrete pattern categories, and the introduction of pattern identifiability as a measurable property derived from the confidence intervals of aggregation indices. I formulate statistical decision rules based on whether the confidence interval of an estimated aggregation index fully exceeds or encompasses critical threshold values, and define a statistically credible pattern only when such criteria are met. Furthermore, I derive the theoretical concept of minimum distinguishable sample size, offering a direct quantitative link between sampling effort and the resolvability of spatial patterns. A comprehensive analytical methodology is proposed, combining bootstrap and Bayesian uncertainty estimation, multiscale pattern fingerprinting, and a five-step decision workflow. By moving beyond the traditional aggregated versus random dichotomy and embracing a rigorous treatment of uncertainty, this work establishes a new standard for the inference of spatial processes governing rare species distributions and provides practical guidance for conservation monitoring design.

Keywords rare species;spatial distribution pattern;aggregation index;scale dependence;estimation uncertainty;effective discrimination.


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